生物灵感机器人中的多式现场驱动执行:一种新兴的分类学和通向混合智能的路线图
Jianping Wang1, Xin Wang2, Shuai Zhou2
1Advanced Vocational Technical College, Shanghai University of Engineering Science, Shanghai 200437, China.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
本综述介绍了刚性-灵活机器人的新型分类法,揭示了混合动力驱动策略在非结构化环境中提高了性能. 生物混合系统具有很高的生物相似性,为智能,适应性强的机器人铺平了道路.
科学领域:
- 机器人与机械工程 机器人与机械工程
- 生物模拟系统 生物模拟系统
- 材料科学 材料科学 材料科学
背景情况:
- 刚性-灵活的合机器人在非结构化环境中提供了优势.
- 缺乏跨物理场的执行策略的系统分析.
- 现有的文献缺乏对这些机器人的全面分类.
研究的目的:
- 在刚性-灵活的机器人中引入一种用于现场控制的驱动路径的新型分类学.
- 用六维框架批判性地检查100多项研究.
- 为开发下一代机器人提供路线图,这些机器人具有体内智能.
主要方法:
- 基于场控制的进化途径开发了一个分类学:机械,电磁,化学和生物混合.
- 对100多项开创性研究进行了批判性审查.
- 使用一个六维框架,涵盖设计,动态和性能.
- 使用雷达图表分析来评估权衡.
主要成果:
- 混合场集成 (例如,气动-化学) 在混乱的环境中提高了40%的抓取强度.
- 生物混合动力执行器与生物模型的运动相似性超过90%.
- 阶段过渡材料使适应性刚度调整 (0.1-5N·mm-1) 能够用于医疗应用.
- 确定了能源效率,响应速度和可扩展性之间的基本权衡.
结论:
- 多领域的协同作用和生物灵感的适应性对于下一代机器人至关重要.
- 拟议的分类学为未来的研发提供了一个明确的路线图.
- 混合动力驱动策略显著提高了机器人的性能和适应性.
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